Intelligent voice recognizing method, apparatus, and intelligent computing device

ABSTRACT

Disclosed are an intelligent voice recognizing method, a voice recognizing device, and an intelligent computing device. According to an embodiment of the present invention, an intelligent voice recognizing method of a voice recognizing device may obtain a microphone detection signal, recognize a user&#39;s voice from the microphone detection signal based on a pre-learned speech recognition model, output information related to a result of recognition of the user&#39;s voice, and update the speech recognition model based on the output speech recognition result information, easily updating the speech recognition model for speech recognition based on the speech recognition result information which is intuitively shown to the user. According to the present invention, one or more of the voice recognizing device, intelligent computing device, and server may be related to artificial intelligence (AI) modules, unmanned aerial vehicles (UAVs), robots, augmented reality (AR) devices, virtual reality (VR) devices, and 5G service-related devices.

CROSS REFERENCE

This application is based on and claims priority under 35 U.S.C. 119 toKorean Patent Application No. 10-2019-0101776, filed on Aug. 20, 2019,in the Korean Intellectual Property Office, the disclosure of which isherein incorporated by reference in its entirety.

TECHNICAL FIELD

The present invention relates to an intelligent voice recognizingmethod, apparatus, and intelligent computing device, and morespecifically, to an intelligent voice recognizing method, apparatus, andintelligent computing device for precisely recognizing a user's voice.

DESCRIPTION OF RELATED ART

A voice output device can convert a user's voice into text, can analyzethe meaning of a message included in the text, and can output a sound ofa different form based on a result of the analysis.

Examples of the voice output device may include a home robot of a homeIoT system and an artificial intelligence (AI) speaker using anartificial intelligence technique.

SUMMARY

An object of the present invention is to meet the needs and solve theproblems.

The present invention also aims to implement an intelligent voicerecognizing method, apparatus, and intelligent computing device forintelligently recognizing a user's voice.

According to an embodiment of the present invention, an intelligentvoice recognizing method comprises obtaining a microphone detectionsignal; recognizing a user's voice from the microphone detection signalbased on a pre-learned speech recognition model and generatinginformation related to a result of recognition of the user's voice,wherein the speech recognition model is updated based on the generatedspeech recognition result information.

The speech recognition result information may include informationrelated to whether the speech recognition succeeds.

The information related to whether the speech recognition succeeds mayinclude text information generated by recognizing the voice.

The information related to whether the speech recognition succeeds mayinclude information related to the number of words included in the textinformation.

The method further comprises performing post-processing on the generatedspeech recognition result information, wherein the speech recognitionresult information may include information related to a result of thepost-processing.

The information related to the result of the post-processing may includeinformation related to a number of times in which the text informationgenerated by the post-processing is modified.

The speech recognition result information may include informationrelated to a speed at which the speech recognition result information isgenerated.

The method further comprises receiving, from a network, downlink controlinformation (DCI) used for scheduling transmission of the speechrecognition result information and transmitting the speech recognitionresult information to the network based on the DCI.

The method further comprises performing an initial access procedure withthe network based on a synchronization signal block (SSB), andtransmitting the speech recognition result information to the networkvia a physical uplink shared channel (PUSCH), whereindemodulation-reference signals (DM-RSs) of the SSB and the PUSCH arequasi co-located (QCL) for QCL type D.

The method further comprises controlling a communication module totransmit the speech recognition result information to an artificialintelligence (AI) processor included in the network and controlling thecommunication module to receive AI-processed information from the AIprocessor, wherein the AI-processed information includes a parameter ofthe speech recognition model updated based on the speech recognitionresult information.

According to an embodiment of the present invention, an intelligentvoice recognizing device comprises at least one microphone detecting anexternal signal and a processor recognizing a user's voice from amicrophone detection signal obtained via the at least one microphonebased on a pre-learned speech recognition model and generatinginformation related to a result of recognition of the user's voice,wherein the speech recognition model is updated based on the generatedspeech recognition result information.

The speech recognition result information may include informationrelated to whether the speech recognition succeeds.

The information related to whether the speech recognition succeeds mayinclude text information generated by recognizing the voice.

The information related to whether the speech recognition succeeds mayinclude information related to the number of words included in the textinformation.

The processor may perform post-processing on the generated speechrecognition result information, wherein the speech recognition resultinformation may include information related to a result of thepost-processing.

The information related to the result of the post-processing may includeinformation related to a number of times in which the text informationgenerated by the post-processing is modified.

The speech recognition result information may include informationrelated to a speed at which the speech recognition result information isgenerated.

The voice recognizing device may further comprise a communication modulefor performing wireless communication with an external device, whereinthe processor receives, from a network through the communication module,downlink control information (DCI) used for scheduling transmission ofthe speech recognition result information and transmits the speechrecognition result information through the communication module to thenetwork based on the DCI.

The processor may perform an initial access procedure with the networkbased on a synchronization signal block (SSB) through the communicationmodule and transmits, through the communication module to the network,the speech recognition result information via a PUSCH, anddemodulation-reference signals (DM-RSs) of the SSB and the PUSCH may bequasi co-located (QCL) for QCL type D.

The processor may control the communication module to transmit thespeech recognition result information to an artificial intelligence (AI)processor included in the network and control the communication moduleto receive AI-processed information from the AI processor, wherein theAI-processed information may include a parameter of the speechrecognition model updated based on the speech recognition resultinformation.

According to another embodiment of the present invention, there isprovided a non-transitory computer-readable medium storing acomputer-executable component configured to be executed by one or moreprocessors of a computing device, the computer-executable componentcomprising obtaining a microphone detection signal, recognizing a user'svoice from a microphone detection signal obtained via the at least onemicrophone based on a pre-learned speech recognition model, andgenerating information related to a result of recognition of the user'svoice, wherein the speech recognition model is updated based on thegenerated speech recognition result information.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, included as part of the detailed descriptionin order to provide a thorough understanding of the present invention,provide embodiments of the present invention and together with thedescription, describe the technical features of the present invention.

FIG. 1 shows a block diagram of a wireless communication system to whichmethods proposed in the disclosure are applicable.

FIG. 2 shows an example of a signal transmission/reception method in awireless communication system.

FIG. 3 shows an example of basic operations of an user equipment and a5G network in a 5G communication system.

FIG. 4 shows an example of a schematic block diagram in which atext-to-speech (TTS) method according to an embodiment of the presentinvention is implemented.

FIG. 5 shows a block diagram of an AI device that may be applied to oneembodiment of the present invention.

FIG. 6 shows an exemplary block diagram of a speech output apparatusaccording to an embodiment of the present invention.

FIG. 7 shows a schematic block diagram of a text-to-speech (TTS) devicein a TTS system according to an embodiment of the present invention.

FIG. 8 shows a schematic block diagram of a TTS device in a TTS systemenvironment according to an embodiment of the present invention.

FIG. 9 is a schematic block diagram of an AI processor capable ofperforming emotion classification information-based TTS according to anembodiment of the present invention.

FIG. 10 is a flowchart illustrating a voice recognizing method accordingto an embodiment of the present invention;

FIG. 11 illustrates a process of updating a speech recognition modelaccording to an embodiment of the present invention;

FIG. 12 illustrates information related to whether speech recognitionsucceeds as an example of speech recognition result information;

FIG. 13 illustrates information related to a post-processing result of aspeech recognition result as another example of the speech recognitionresult information;

FIG. 14 illustrates information related to a speech recognition time asstill another example of the speech recognition result information;

FIG. 15 is a flowchart illustrating a process of performing the speechrecognition model update of FIG. 10 via AI processing; and

FIG. 16 is a flowchart illustrating a process of performing the modelupdate (S172) of FIG. 15 via a 5G network.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

Hereinafter, embodiments of the disclosure will be described in detailwith reference to the attached drawings. The same or similar componentsare given the same reference numbers and redundant description thereofis omitted. The suffixes “module” and “unit” of elements herein are usedfor convenience of description and thus can be used interchangeably anddo not have any distinguishable meanings or functions. Further, in thefollowing description, if a detailed description of known techniquesassociated with the present invention would unnecessarily obscure thegist of the present invention, detailed description thereof will beomitted. In addition, the attached drawings are provided for easyunderstanding of embodiments of the disclosure and do not limittechnical spirits of the disclosure, and the embodiments should beconstrued as including all modifications, equivalents, and alternativesfalling within the spirit and scope of the embodiments.

While terms, such as “first”, “second”, etc., may be used to describevarious components, such components must not be limited by the aboveterms. The above terms are used only to distinguish one component fromanother.

When an element is “coupled” or “connected” to another element, itshould be understood that a third element may be present between the twoelements although the element may be directly coupled or connected tothe other element. When an element is “directly coupled” or “directlyconnected” to another element, it should be understood that no elementis present between the two elements.

The singular forms are intended to include the plural forms as well,unless the context clearly indicates otherwise.

In addition, in the specification, it will be further understood thatthe terms “comprise” and “include” specify the presence of statedfeatures, integers, steps, operations, elements, components, and/orcombinations thereof, but do not preclude the presence or addition ofone or more other features, integers, steps, operations, elements,components, and/or combinations.

Hereinafter, 5G communication (5th generation mobile communication)required by an apparatus requiring AI processed information and/or an AIprocessor will be described through paragraphs A through G.

A. Example of Block Diagram of UE and 5G Network

FIG. 1 is a block diagram of a wireless communication system to whichmethods proposed in the disclosure are applicable.

Referring to FIG. 1, a device (AI device) including an AI module isdefined as a first communication device (910 of FIG. 1), and a processor911 can perform detailed AI operation.

A 5G network including another device(AI server) communicating with theAI device is defined as a second communication device (920 of FIG. 1),and a processor 921 can perform detailed AI operations.

The 5G network may be represented as the first communication device andthe AI device may be represented as the second communication device.

For example, the first communication device or the second communicationdevice may be a base station, a network node, a transmission terminal, areception terminal, a wireless device, a wireless communication device,an autonomous device, or the like.

For example, the first communication device or the second communicationdevice may be a base station, a network node, a transmission terminal, areception terminal, a wireless device, a wireless communication device,a vehicle, a vehicle having an autonomous function, a connected car, adrone (Unmanned Aerial Vehicle, UAV), and AI (Artificial Intelligence)module, a robot, an AR (Augmented Reality) device, a VR (VirtualReality) device, an MR (Mixed Reality) device, a hologram device, apublic safety device, an MTC device, an IoT device, a medical device, aFin Tech device (or financial device), a security device, aclimate/environment device, a device associated with 5G services, orother devices associated with the fourth industrial revolution field.

For example, a terminal or user equipment (UE) may include a cellularphone, a smart phone, a laptop computer, a digital broadcast terminal,personal digital assistants (PDAs), a portable multimedia player (PMP),a navigation device, a slate PC, a tablet PC, an ultrabook, a wearabledevice (e.g., a smartwatch, a smart glass and a head mounted display(HMD)), etc. For example, the HMD may be a display device worn on thehead of a user. For example, the HMD may be used to realize VR, AR orMR. For example, the drone may be a flying object that flies by wirelesscontrol signals without a person therein. For example, the VR device mayinclude a device that implements objects or backgrounds of a virtualworld. For example, the AR device may include a device that connects andimplements objects or background of a virtual world to objects,backgrounds, or the like of a real world. For example, the MR device mayinclude a device that unites and implements objects or background of avirtual world to objects, backgrounds, or the like of a real world. Forexample, the hologram device may include a device that implements360-degree 3D images by recording and playing 3D information using theinterference phenomenon of light that is generated by two lasers meetingeach other which is called holography. For example, the public safetydevice may include an image repeater or an imaging device that can beworn on the body of a user. For example, the MTC device and the IoTdevice may be devices that do not require direct interference oroperation by a person. For example, the MTC device and the IoT devicemay include a smart meter, a bending machine, a thermometer, a smartbulb, a door lock, various sensors, or the like. For example, themedical device may be a device that is used to diagnose, treat,attenuate, remove, or prevent diseases. For example, the medical devicemay be a device that is used to diagnose, treat, attenuate, or correctinjuries or disorders. For example, the medial device may be a devicethat is used to examine, replace, or change structures or functions. Forexample, the medical device may be a device that is used to controlpregnancy. For example, the medical device may include a device formedical treatment, a device for operations, a device for (external)diagnose, a hearing aid, an operation device, or the like. For example,the security device may be a device that is installed to prevent adanger that is likely to occur and to keep safety. For example, thesecurity device may be a camera, a CCTV, a recorder, a black box, or thelike. For example, the Fin Tech device may be a device that can providefinancial services such as mobile payment.

Referring to FIG. 1, the first communication device 910 and the secondcommunication device 920 include processors 911 and 921, memories 914and 924, one or more Tx/Rx radio frequency (RF) modules 915 and 925, Txprocessors 912 and 922, Rx processors 913 and 923, and antennas 916 and926. The Tx/Rx module is also referred to as a transceiver. Each Tx/Rxmodule 915 transmits a signal through each antenna 926. The processorimplements the aforementioned functions, processes and/or methods. Theprocessor 921 may be related to the memory 924 that stores program codeand data. The memory may be referred to as a computer-readable medium.More specifically, the Tx processor 912 implements various signalprocessing functions with respect to L1 (i.e., physical layer) in DL(communication from the first communication device to the secondcommunication device). The Rx processor implements various signalprocessing functions of L1 (i.e., physical layer).

UL (communication from the second communication device to the firstcommunication device) is processed in the first communication device 910in a way similar to that described in association with a receiverfunction in the second communication device 920. Each Tx/Rx module 925receives a signal through each antenna 926. Each Tx/Rx module providesRF carriers and information to the Rx processor 923. The processor 921may be related to the memory 924 that stores program code and data. Thememory may be referred to as a computer-readable medium.

B. Signal Transmission/Reception Method in Wireless Communication System

FIG. 2 is a diagram showing an example of a signaltransmission/reception method in a wireless communication system.

Referring to FIG. 2, when a UE is powered on or enters a new cell, theUE performs an initial cell search operation such as synchronizationwith a BS (S201). For this operation, the UE can receive a primarysynchronization channel (P-SCH) and a secondary synchronization channel(S-SCH) from the BS to synchronize with the BS and obtain informationsuch as a cell ID. In LTE and NR systems, the P-SCH and S-SCH arerespectively called a primary synchronization signal (PSS) and asecondary synchronization signal (SSS). After initial cell search, theUE can obtain broadcast information in the cell by receiving a physicalbroadcast channel (PBCH) from the BS. Further, the UE can receive adownlink reference signal (DL RS) in the initial cell search step tocheck a downlink channel state. After initial cell search, the UE canobtain more detailed system information by receiving a physical downlinkshared channel (PDSCH) according to a physical downlink control channel(PDCCH) and information included in the PDCCH (S202).

Meanwhile, when the UE initially accesses the BS or has no radioresource for signal transmission, the UE can perform a random accessprocedure (RACH) for the BS (steps S203 to S206). To this end, the UEcan transmit a specific sequence as a preamble through a physical randomaccess channel (PRACH) (S203 and S205) and receive a random accessresponse (RAR) message for the preamble through a PDCCH and acorresponding PDSCH (S204 and S206). In the case of a contention-basedRACH, a contention resolution procedure may be additionally performed.

After the UE performs the above-described process, the UE can performPDCCH/PDSCH reception (S207) and physical uplink shared channel(PUSCH)/physical uplink control channel (PUCCH) transmission (S208) asnormal uplink/downlink signal transmission processes. Particularly, theUE receives downlink control information (DCI) through the PDCCH. The UEmonitors a set of PDCCH candidates in monitoring occasions set for oneor more control element sets (CORESET) on a serving cell according tocorresponding search space configurations. A set of PDCCH candidates tobe monitored by the UE is defined in terms of search space sets, and asearch space set may be a common search space set or a UE-specificsearch space set. CORESET includes a set of (physical) resource blockshaving a duration of one to three OFDM symbols. A network can configurethe UE such that the UE has a plurality of CORESETs. The UE monitorsPDCCH candidates in one or more search space sets. Here, monitoringmeans attempting decoding of PDCCH candidate(s) in a search space. Whenthe UE has successfully decoded one of PDCCH candidates in a searchspace, the UE determines that a PDCCH has been detected from the PDCCHcandidate and performs PDSCH reception or PUSCH transmission on thebasis of DCI in the detected PDCCH. The PDCCH can be used to schedule DLtransmissions over a PDSCH and UL transmissions over a PUSCH. Here, theDCI in the PDCCH includes downlink assignment (i.e., downlink grant (DLgrant)) related to a physical downlink shared channel and including atleast a modulation and coding format and resource allocationinformation, or an uplink grant (UL grant) related to a physical uplinkshared channel and including a modulation and coding format and resourceallocation information.

An initial access (IA) procedure in a 5G communication system will beadditionally described with reference to FIG. 2.

The UE can perform cell search, system information acquisition, beamalignment for initial access, and DL measurement on the basis of an SSB.The SSB is interchangeably used with a synchronization signal/physicalbroadcast channel (SS/PBCH) block.

The SSB includes a PSS, an SSS and a PBCH. The SSB is configured in fourconsecutive OFDM symbols, and a PSS, a PBCH, an SSS/PBCH or a PBCH istransmitted for each OFDM symbol. Each of the PSS and the SSS includesone OFDM symbol and 127 subcarriers, and the PBCH includes 3 OFDMsymbols and 576 subcarriers.

Cell search refers to a process in which a UE obtains time/frequencysynchronization of a cell and detects a cell identifier (ID) (e.g.,physical layer cell ID (PCI)) of the cell. The PSS is used to detect acell ID in a cell ID group and the SSS is used to detect a cell IDgroup. The PBCH is used to detect an SSB (time) index and a half-frame.

There are 336 cell ID groups and there are 3 cell IDs per cell ID group.A total of 1008 cell IDs are present. Information on a cell ID group towhich a cell ID of a cell belongs is provided/obtained through an SSS ofthe cell, and information on the cell ID among 336 cell ID groups isprovided/obtained through a PSS.

The SSB is periodically transmitted in accordance with SSB periodicity.A default SSB periodicity assumed by a UE during initial cell search isdefined as 20 ms. After cell access, the SSB periodicity can be set toone of {5 ms, 10 ms, 20 ms, 40 ms, 80 ms, 160 ms} by a network (e.g., aBS).

Next, acquisition of system information (SI) will be described.

SI is divided into a master information block (MIB) and a plurality ofsystem information blocks (SIBs). SI other than the MIB may be referredto as remaining minimum system information. The MIB includesinformation/parameter for monitoring a PDCCH that schedules a PDSCHcarrying SIB1 (SystemInformationBlock1) and is transmitted by a BSthrough a PBCH of an SSB. SIB1 includes information related toavailability and scheduling (e.g., transmission periodicity andSI-window size) of the remaining SIBs (hereinafter, SIBx, x is aninteger equal to or greater than 2). SiBx is included in an SI messageand transmitted over a PDSCH. Each SI message is transmitted within aperiodically generated time window (i.e., SI-window).

A random access (RA) procedure in a 5G communication system will beadditionally described with reference to FIG. 2.

A random access procedure is used for various purposes. For example, therandom access procedure can be used for network initial access,handover, and UE-triggered UL data transmission. A UE can obtain ULsynchronization and UL transmission resources through the random accessprocedure. The random access procedure is classified into acontention-based random access procedure and a contention-free randomaccess procedure. A detailed procedure for the contention-based randomaccess procedure is as follows.

A UE can transmit a random access preamble through a PRACH as Msg1 of arandom access procedure in UL. Random access preamble sequences havingdifferent two lengths are supported. A long sequence length 839 isapplied to subcarrier spacings of 1.25 kHz and 5 kHz and a shortsequence length 139 is applied to subcarrier spacings of 15 kHz, 30 kHz,60 kHz and 120 kHz.

When a BS receives the random access preamble from the UE, the BStransmits a random access response (RAR) message (Msg2) to the UE. APDCCH that schedules a PDSCH carrying a RAR is CRC masked by a randomaccess (RA) radio network temporary identifier (RNTI) (RA-RNTI) andtransmitted. Upon detection of the PDCCH masked by the RA-RNTI, the UEcan receive a RAR from the PDSCH scheduled by DCI carried by the PDCCH.The UE checks whether the RAR includes random access responseinformation with respect to the preamble transmitted by the UE, that is,Msg1. Presence or absence of random access information with respect toMsg1 transmitted by the UE can be determined according to presence orabsence of a random access preamble ID with respect to the preambletransmitted by the UE. If there is no response to Msg1, the UE canretransmit the RACH preamble less than a predetermined number of timeswhile performing power ramping. The UE calculates PRACH transmissionpower for preamble retransmission on the basis of most recent pathlossand a power ramping counter.

The UE can perform UL transmission through Msg3 of the random accessprocedure over a physical uplink shared channel on the basis of therandom access response information. Msg3 can include an RRC connectionrequest and a UE ID. The network can transmit Msg4 as a response toMsg3, and Msg4 can be handled as a contention resolution message on DL.The UE can enter an RRC connected state by receiving Msg4.

C. Beam Management (BM) Procedure of 5G Communication System

A BM procedure can be divided into (1) a DL MB procedure using an SSB ora CSI-RS and (2) a UL BM procedure using a sounding reference signal(SRS). In addition, each BM procedure can include Tx beam swiping fordetermining a Tx beam and Rx beam swiping for determining an Rx beam.

The DL BM procedure using an SSB will be described.

Configuration of a beam report using an SSB is performed when channelstate information (CSI)/beam is configured in RRC_CONNECTED.

A UE receives a CSI-ResourceConfig IE including CSI-SSB-ResourceSetListfor SSB resources used for BM from a BS. The RRC parameter“csi-SSB-ResourceSetList” represents a list of SSB resources used forbeam management and report in one resource set. Here, an SSB resourceset can be set as {SSBx1, SSBx2, SSBx3, SSBx4, . . . }. An SSB index canbe defined in the range of 0 to 63.

The UE receives the signals on SSB resources from the BS on the basis ofthe CSI-SSB-Resource SetList.

When CSI-RS reportConfig with respect to a report on SSBRI and referencesignal received power (RSRP) is set, the UE reports the best SSBRI andRSRP corresponding thereto to the BS. For example, when reportQuantityof the CSI-RS reportConfig IE is set to ‘ssb-Index-RSRP’, the UE reportsthe best SSBRI and RSRP corresponding thereto to the BS.

When a CSI-RS resource is configured in the same OFDM symbols as an SSBand ‘QCL-TypeD’ is applicable, the UE can assume that the CSI-RS and theSSB are quasi co-located (QCL) from the viewpoint of ‘QCL-TypeD’. Here,QCL-TypeD may mean that antenna ports are quasi co-located from theviewpoint of a spatial Rx parameter. When the UE receives signals of aplurality of DL antenna ports in a QCL-TypeD relationship, the same Rxbeam can be applied.

Next, a DL BM procedure using a CSI-RS will be described.

An Rx beam determination (or refinement) procedure of a UE and a Tx beamswiping procedure of a BS using a CSI-RS will be sequentially described.A repetition parameter is set to ‘ON’ in the Rx beam determinationprocedure of a UE and set to ‘OFF’ in the Tx beam swiping procedure of aBS.

First, the Rx beam determination procedure of a UE will be described.

The UE receives an NZP CSI-RS resource set IE including an RRC parameterwith respect to ‘repetition’ from a BS through RRC signaling Here, theRRC parameter ‘repetition’ is set to ‘ON’.

The UE repeatedly receives signals on resources in a CSI-RS resource setin which the RRC parameter ‘repetition’ is set to ‘ON’ in different OFDMsymbols through the same Tx beam (or DL spatial domain transmissionfilters) of the BS.

The UE determines an RX beam thereof.

The UE skips a CSI report. That is, the UE can skip a CSI report whenthe RRC parameter ‘repetition’ is set to ‘ON’.

Next, the Tx beam determination procedure of a BS will be described.

A UE receives an NZP CSI-RS resource set IE including an RRC parameterwith respect to ‘repetition’ from the BS through RRC signaling. Here,the RRC parameter ‘repetition’ is related to the Tx beam swipingprocedure of the BS when set to ‘OFF’.

The UE receives signals on resources in a CSI-RS resource set in whichthe RRC parameter ‘repetition’ is set to ‘OFF’ in different DL spatialdomain transmission filters of the BS.

The UE selects (or determines) a best beam.

The UE reports an ID (e.g., CRI) of the selected beam and relatedquality information (e.g., RSRP) to the BS. That is, when a CSI-RS istransmitted for BM, the UE reports a CRI and RSRP with respect theretoto the BS.

Next, the UL BM procedure using an SRS will be described.

A UE receives RRC signaling (e.g., SRS-Config IE) including a (RRCparameter) purpose parameter set to ‘beam management” from a BS. TheSRS-Config IE is used to set SRS transmission. The SRS-Config IEincludes a list of SRS-Resources and a list of SRS-ResourceSets. EachSRS resource set refers to a set of SRS-resources.

The UE determines Tx beamforming for SRS resources to be transmitted onthe basis of SRS-SpatialRelation Info included in the SRS-Config IE.Here, SRS-SpatialRelation Info is set for each SRS resource andindicates whether the same beamforming as that used for an SSB, a CSI-RSor an SRS will be applied for each SRS resource.

When SRS-SpatialRelationInfo is set for SRS resources, the samebeamforming as that used for the SSB, CSI-RS or SRS is applied. However,when SRS-SpatialRelationInfo is not set for SRS resources, the UEarbitrarily determines Tx beamforming and transmits an SRS through thedetermined Tx beamforming.

Next, a beam failure recovery (BFR) procedure will be described.

In a beamformed system, radio link failure (RLF) may frequently occurdue to rotation, movement or beamforming blockage of a UE. Accordingly,NR supports BFR in order to prevent frequent occurrence of RLF. BFR issimilar to a radio link failure recovery procedure and can be supportedwhen a UE knows new candidate beams. For beam failure detection, a BSconfigures beam failure detection reference signals for a UE, and the UEdeclares beam failure when the number of beam failure indications fromthe physical layer of the UE reaches a threshold set through RRCsignaling within a period set through RRC signaling of the BS. Afterbeam failure detection, the UE triggers beam failure recovery byinitiating a random access procedure in a PCell and performs beamfailure recovery by selecting a suitable beam. (When the BS providesdedicated random access resources for certain beams, these areprioritized by the UE). Completion of the aforementioned random accessprocedure is regarded as completion of beam failure recovery.

D. URLLC (Ultra-Reliable and Low Latency Communication)

URLLC transmission defined in NR can refer to (1) a relatively lowtraffic size, (2) a relatively low arrival rate, (3) extremely lowlatency requirements (e.g., 0.5 and 1 ms), (4) relatively shorttransmission duration (e.g., 2 OFDM symbols), (5) urgentservices/messages, etc. In the case of UL, transmission of traffic of aspecific type (e.g., URLLC) needs to be multiplexed with anothertransmission (e.g., eMBB) scheduled in advance in order to satisfy morestringent latency requirements. In this regard, a method of providinginformation indicating preemption of specific resources to a UEscheduled in advance and allowing a URLLC UE to use the resources for ULtransmission is provided.

NR supports dynamic resource sharing between eMBB and URLLC. eMBB andURLLC services can be scheduled on non-overlapping time/frequencyresources, and URLLC transmission can occur in resources scheduled forongoing eMBB traffic. An eMBB UE may not ascertain whether PDSCHtransmission of the corresponding UE has been partially punctured andthe UE may not decode a PDSCH due to corrupted coded bits. In view ofthis, NR provides a preemption indication. The preemption indication mayalso be referred to as an interrupted transmission indication.

With regard to the preemption indication, a UE receivesDownlinkPreemption IE through RRC signaling from a BS. When the UE isprovided with DownlinkPreemption IE, the UE is configured with INT-RNTIprovided by a parameter int-RNTI in DownlinkPreemption IE for monitoringof a PDCCH that conveys DCI format 2_1. The UE is additionallyconfigured with a corresponding set of positions for fields in DCIformat 2_1 according to a set of serving cells and positionInDCI byINT-ConfigurationPerServing Cell including a set of serving cell indexesprovided by servingCellID, configured having an information payload sizefor DCI format 2_1 according to dci-Payloadsize, and configured withindication granularity of time-frequency resources according totimeFrequency Sect.

The UE receives DCI format 2_1 from the BS on the basis of theDownlinkPreemption IE.

When the UE detects DCI format 2_1 for a serving cell in a configuredset of serving cells, the UE can assume that there is no transmission tothe UE in PRBs and symbols indicated by the DCI format 2_1 in a set ofPRBs and a set of symbols in a last monitoring period before amonitoring period to which the DCI format 2_1 belongs. For example, theUE assumes that a signal in a time-frequency resource indicatedaccording to preemption is not DL transmission scheduled therefor anddecodes data on the basis of signals received in the remaining resourceregion.

E. mMTC (massive MTC)

mMTC (massive Machine Type Communication) is one of 5G scenarios forsupporting a hyper-connection service providing simultaneouscommunication with a large number of UEs. In this environment, a UEintermittently performs communication with a very low speed andmobility. Accordingly, a main goal of mMTC is operating a UE for a longtime at a low cost. With respect to mMTC, 3GPP deals with MTC and NB(NarrowBand)-IoT.

mMTC has features such as repetitive transmission of a PDCCH, a PUCCH, aPDSCH (physical downlink shared channel), a PUSCH, etc., frequencyhopping, retuning, and a guard period.

That is, a PUSCH (or a PUCCH (particularly, a long PUCCH) or a PRACH)including specific information and a PDSCH (or a PDCCH) including aresponse to the specific information are repeatedly transmitted.Repetitive transmission is performed through frequency hopping, and forrepetitive transmission, (RF) retuning from a first frequency resourceto a second frequency resource is performed in a guard period and thespecific information and the response to the specific information can betransmitted/received through a narrowband (e.g., 6 resource blocks (RBs)or 1 RB).

F. Basic Operation of AI Processing Using 5G Communication

FIG. 3 shows an example of basic operations of AI processing in a 5Gcommunication system.

The UE transmits specific information to the 5G network (S1). The 5Gnetwork may perform 5G processing related to the specific information(S2). Here, the 5G processing may include AI processing. And the 5Gnetwork may transmit response including AI processing result to UE (S3).

G. Applied Operations Between UE and 5G Network in 5G CommunicationSystem

Hereinafter, the operation of an autonomous vehicle using 5Gcommunication will be described in more detail with reference towireless communication technology (BM procedure, URLLC, mMTC, etc.)described in FIGS. 1 and 2.

First, a basic procedure of an applied operation to which a methodproposed by the present invention which will be described later and eMBBof 5G communication are applied will be described.

As in steps S1 and S3 of FIG. 3, the autonomous vehicle performs aninitial access procedure and a random access procedure with the 5Gnetwork prior to step S1 of FIG. 3 in order to transmit/receive signals,information and the like to/from the 5G network.

More specifically, the autonomous vehicle performs an initial accessprocedure with the 5G network on the basis of an SSB in order to obtainDL synchronization and system information. A beam management (BM)procedure and a beam failure recovery procedure may be added in theinitial access procedure, and quasi-co-location (QCL) relation may beadded in a process in which the autonomous vehicle receives a signalfrom the 5G network.

In addition, the autonomous vehicle performs a random access procedurewith the 5G network for UL synchronization acquisition and/or ULtransmission. The 5G network can transmit, to the autonomous vehicle, aUL grant for scheduling transmission of specific information.Accordingly, the autonomous vehicle transmits the specific informationto the 5G network on the basis of the UL grant. In addition, the 5Gnetwork transmits, to the autonomous vehicle, a DL grant for schedulingtransmission of 5G processing results with respect to the specificinformation. Accordingly, the 5G network can transmit, to the autonomousvehicle, information (or a signal) related to remote control on thebasis of the DL grant.

Next, a basic procedure of an applied operation to which a methodproposed by the present invention which will be described later andURLLC of 5G communication are applied will be described.

As described above, an autonomous vehicle can receive DownlinkPreemptionIE from the 5G network after the autonomous vehicle performs an initialaccess procedure and/or a random access procedure with the 5G network.Then, the autonomous vehicle receives DCI format 2_1 including apreemption indication from the 5G network on the basis ofDownlinkPreemption IE. The autonomous vehicle does not perform (orexpect or assume) reception of eMBB data in resources (PRBs and/or OFDMsymbols) indicated by the preemption indication. Thereafter, when theautonomous vehicle needs to transmit specific information, theautonomous vehicle can receive a UL grant from the 5G network.

Next, a basic procedure of an applied operation to which a methodproposed by the present invention which will be described later and mMTCof 5G communication are applied will be described.

Description will focus on parts in the steps of FIG. 3 which are changedaccording to application of mMTC.

In step S1 of FIG. 3, the autonomous vehicle receives a UL grant fromthe 5G network in order to transmit specific information to the 5Gnetwork. Here, the UL grant may include information on the number ofrepetitions of transmission of the specific information and the specificinformation may be repeatedly transmitted on the basis of theinformation on the number of repetitions. That is, the autonomousvehicle transmits the specific information to the 5G network on thebasis of the UL grant. Repetitive transmission of the specificinformation may be performed through frequency hopping, the firsttransmission of the specific information may be performed in a firstfrequency resource, and the second transmission of the specificinformation may be performed in a second frequency resource. Thespecific information can be transmitted through a narrowband of 6resource blocks (RBs) or 1 RB.

The above-described 5G communication technology can be combined withmethods proposed in the present invention which will be described laterand applied or can complement the methods proposed in the presentinvention to make technical features of the methods concrete and clear.

H. Voice Output System and AI Processing

FIG. 4 illustrates a block diagram of a schematic system in which avoice output method is implemented according to an embodiment of thepresent invention.

Referring to FIG. 4, a system in which a voice output method isimplemented according to an embodiment of the present invention mayinclude as a voice output apparatus 10, a network system 16, and atext-to-to-speech (TTS) system as a speech synthesis engine.

The at least one voice output device 10 may include a mobile phone 11, aPC 12, a notebook computer 13, and other server devices 14. The PC 12and notebook computer 13 may connect to at least one network system 16via a wireless access point 15. According to an embodiment of thepresent invention, the voice output apparatus 10 may include an audiobook and a smart speaker.

Meanwhile, the TTS system 18 may be implemented in a server included ina network, or may be implemented by on-device processing and embedded inthe voice output device 10. In the exemplary embodiment of the presentinvention, it is assumed that the TTS system 18 is implemented in thevoice output device 10.

FIG. 5 shows a block diagram of an AI device that may be applied to oneembodiment of the present invention.

The AI device 20 may include an electronic device including an AI modulecapable of performing AI processing or a server including the AI module.In addition, the AI device 20 may be included in at least a part of thevoice output device 10 illustrated in FIG. 4 and may be provided toperform at least some of the AI processing together.

The above-described AI processing may include all operations related tospeech recognition of the voice recognizing device 10 of FIG. 5. Forexample, the AI processing may be the process of analyzing speechrecognition result information of the voice recognizing device 10 tothereby output parameters of the updated speech recognition model.

The AI device 20 may include an AI processor 21, a memory 25, and/or acommunication unit 27.

The AI device 20 is a computing device capable of learning neuralnetworks, and may be implemented as various electronic devices such as aserver, a desktop PC, a notebook PC, a tablet PC, and the like.

The AI processor 21 may learn a neural network using a program stored inthe memory 25.

In particular, the AI processor 21 may learn a neural network forobtaining estimated noise information by analyzing the operating stateof each voice output device. In this case, the neural network foroutputting estimated noise information may be designed to simulate thehuman's brain structure on a computer, and may include a plurality ofnetwork nodes having weight and simulating the neurons of the human'sneural network.

The plurality of network nodes can transmit and receive data inaccordance with each connection relationship to simulate the synapticactivity of neurons in which neurons transmit and receive signalsthrough synapses. Here, the neural network may include a deep learningmodel developed from a neural network model. In the deep learning model,a plurality of network nodes is positioned in different layers and cantransmit and receive data in accordance with a convolution connectionrelationship. The neural network, for example, includes various deeplearning techniques such as deep neural networks (DNN), convolutionaldeep neural networks (CNN), recurrent neural networks (RNN), arestricted boltzmann machine (RBM), deep belief networks (DBN), and adeep Q-network, and can be applied to fields such as computer vision,voice output, natural language processing, and voice/signal processing.

Meanwhile, a processor that performs the functions described above maybe a general purpose processor (e.g., a CPU), but may be an AI-onlyprocessor (e.g., a GPU) for artificial intelligence learning.

The memory 25 can store various programs and data for the operation ofthe AI device 20. The memory 25 may be a nonvolatile memory, a volatilememory, a flash-memory, a hard disk drive (HDD), a solid state drive(SDD), or the like. The memory 25 is accessed by the AI processor 21 andreading-out/recording/correcting/deleting/updating, etc. of data by theAI processor 21 can be performed. Further, the memory 25 can store aneural network model (e.g., a deep learning model 26) generated througha learning algorithm for data classification/recognition according to anembodiment of the present invention.

Meanwhile, the AI processor 21 may include a data learning unit 22 thatlearns a neural network for data classification/recognition. The datalearning unit 22 can learn references about what learning data are usedand how to classify and recognize data using the learning data in orderto determine data classification/recognition. The data learning unit 22can learn a deep learning model by obtaining learning data to be usedfor learning and by applying the obtained learning data to the deeplearning model.

The data learning unit 22 may be manufactured in the type of at leastone hardware chip and mounted on the AI device 20. For example, the datalearning unit 22 may be manufactured in a hardware chip type only forartificial intelligence, and may be manufactured as a part of a generalpurpose processor (CPU) or a graphics processing unit (GPU) and mountedon the AI device 20. Further, the data learning unit 22 may beimplemented as a software module. When the data leaning unit 22 isimplemented as a software module (or a program module includinginstructions), the software module may be stored in non-transitorycomputer readable media that can be read through a computer. In thiscase, at least one software module may be provided by an OS (operatingsystem) or may be provided by an application.

The data learning unit 22 may include a learning data obtaining unit 23and a model learning unit 24.

The learning data acquisition unit 23 may obtain training data for aneural network model for classifying and recognizing data. For example,the learning data acquisition unit 23 may obtain message to be input tothe neural network model and/or a feature value, extracted from themessage, as the training data.

The model learning unit 24 can perform learning such that a neuralnetwork model has a determination reference about how to classifypredetermined data, using the obtained learning data. In this case, themodel learning unit 24 can train a neural network model throughsupervised learning that uses at least some of learning data as adetermination reference. Alternatively, the model learning data 24 cantrain a neural network model through unsupervised learning that findsout a determination reference by performing learning by itself usinglearning data without supervision. Further, the model learning unit 24can train a neural network model through reinforcement learning usingfeedback about whether the result of situation determination accordingto learning is correct. Further, the model learning unit 24 can train aneural network model using a learning algorithm including errorback-propagation or gradient decent.

When a neural network model is learned, the model learning unit 24 canstore the learned neural network model in the memory. The model learningunit 24 may store the learned neural network model in the memory of aserver connected with the AI device 20 through a wire or wirelessnetwork.

The data learning unit 22 may further include a learning datapreprocessor (not shown) and a learning data selector (not shown) toimprove the analysis result of a recognition model or reduce resourcesor time for generating a recognition model.

The learning data preprocessor may pre-process an obtained operatingstate so that the obtained operating state may be used for training forrecognizing estimated noise information. For example, the learning datapreprocessor may process an obtained operating state in a preset formatso that the model training unit 24 may use obtained training data fortraining for recognizing estimated noise information.

Furthermore, the training data selection unit may select data fortraining among training data obtained by the learning data acquisitionunit 23 or training data pre-processed by the preprocessor. The selectedtraining data may be provided to the model training unit 24. Forexample, the training data selection unit may select only data for asyllable, included in a specific region, as training data by detectingthe specific region in the feature values of an operating state obtainedby the voice output device 10.

Further, the data learning unit 22 may further include a model estimator(not shown) to improve the analysis result of a neural network model.

The model estimator inputs estimation data to a neural network model,and when an analysis result output from the estimation data does notsatisfy a predetermined reference, it can make the model learning unit22 perform learning again. In this case, the estimation data may be datadefined in advance for estimating a recognition model. For example, whenthe number or ratio of estimation data with an incorrect analysis resultof the analysis result of a recognition model learned with respect toestimation data exceeds a predetermined threshold, the model estimatorcan estimate that a predetermined reference is not satisfied.

The communication unit 27 can transmit the AI processing result by theAI processor 21 to an external electronic device.

Here, the external electronic device may be defined as an autonomousvehicle. Further, the AI device 20 may be defined as another vehicle ora 5G network that communicates with the autonomous vehicle. Meanwhile,the AI device 20 may be implemented by being functionally embedded in anautonomous module included in a vehicle. Further, the 5G network mayinclude a server or a module that performs control related to autonomousdriving.

Meanwhile, the AI device 20 shown in FIG. 5 was functionally separatelydescribed into the AI processor 21, the memory 25, the communicationunit 27, etc., but it should be noted that the aforementioned componentsmay be integrated in one module and referred to as an AI module.

FIG. 6 is an exemplary block diagram of a voice output apparatusaccording to an embodiment of the present invention.

One embodiment of the present invention may include computer readableand computer executable instructions that may be included in the voiceoutput apparatus 10. Although FIG. 6 discloses a plurality of componentsincluded in the voice output apparatus 10, the components not disclosedmay be included in the voice output apparatus 10.

A plurality of voice output apparatuses may be applied to one voiceoutput apparatus. In such a multi-device system the voice outputapparatus may comprise different components for performing variousaspects of voice output processing. The voice output apparatus 10 shownin FIG. 6 is exemplary and may be an independent apparatus or may beimplemented as a component of a larger apparatus or system.

One embodiment of the present invention may be applied to a plurality ofdifferent devices and computer systems, for example, a general purposecomputing system, a server-client computing system, a telephonecomputing system, a laptop computer, a portable terminal, a PDA, atablet computer, and the like. The voice output device 10 may also beapplied to one component of another device or system that provides voiceoutput such as automated teller machines (ATMs), kiosks, globalpositioning systems (GPS), home appliances (eg, refrigerators, ovens,washing machines, etc.), vehicles (vehicles), e-book readers.

As shown in FIG. 6, the voice output apparatus 10 includes acommunication unit 110, an input unit 120, an output unit 130, a memory140, a sensing unit 150, an interface unit 160, and a power supply unit190 and/or processor 170. On the other hand, some of the componentsdisclosed in the voice output apparatus 10 may appear as a singlecomponent several times in one device.

The voice output apparatus 10 may include an address/data bus (notshown) for transferring data between the components of the voice outputapparatus 10. Each component in the voice output apparatus 10 may bedirectly connected to other components through the bus (not shown).Meanwhile, each component in the voice output apparatus 10 may bedirectly connected to the processor 170.

More specifically, the communication unit 110 may include one or moremodules that enable communication between the above components, wirelesscommunication between the electronic device 10 and the wirelesscommunication system, between the electronic device 10 and anotherelectronic device, or between the electronic device 10 and an externalserver. In addition, the communication unit 110 may include one or moremodules for connecting the electronic device 10 to one or more networks.

The communication unit 110 may be a wireless communication device suchas a radio frequency (RF), an infrared (Infrared), Bluetooth, a wirelesslocal area network (WLAN) (Wi-Fi, etc.) or 5G network, a Long TermEvolution (LTE) network, wireless network wireless devices such as WiMANnetworks, 3G networks.

The communication unit 110 may include at least one of a broadcastreceiving module, a mobile communication module, a wireless internetmodule, a short range communication module, and a location informationmodule.

The input unit 120 may include a microphone, a touch input unit, akeyboard, a mouse, a stylus, or another input unit.

In addition, the input unit 120 may include a camera or an image inputunit for inputting an image signal, a microphone or an audio input unitfor inputting an audio signal, and a user input unit (eg, a touch key,push key (mechanical key, etc.)) for receiving information from a user.The voice data or the image data collected by the input unit 120 may beanalyzed and processed as a control command of the user.

The sensing unit 150 may include one or more sensors for sensing atleast one of information in the electronic device 10, surroundingenvironment information surrounding the electronic device 10, and userinformation.

For example, the sensing unit 150 may include at least one of aproximity sensor, an illumination sensor, a touch sensor, anacceleration sensor, a magnetic sensor, and a gravity sensor (G-sensor),gyroscope sensor, motion sensor, RGB sensor, infrared sensor (IRsensor), fingerprint scan sensor, ultrasonic sensor, optical sensor(e.g., imaging means), microphones, battery gauges, environmentalsensors (e.g. barometers, hygrometers, thermometers, radiation sensors,heat sensors, gas sensors, etc.), a chemical sensor (eg, electronicnose, healthcare sensor, biometric sensor, etc.). Meanwhile, theelectronic device 10 disclosed herein may use a combination ofinformation sensed by at least two or more of these sensors.

The output unit 130 may output information (for example, voice)processed by the voice output device 10 or another device. The outputunit 130 may include a speaker, a headphone, or other suitable componentfor propagating voice. As another example, the output unit 130 mayinclude an audio output unit. In addition, the output unit 130 mayinclude a display (visual display or tactile display), audio speakers,headphones, printer or other output unit. The output unit 130 may beintegrated into the voice output apparatus 10 or may be implementedseparately from the voice output apparatus 10.

The output unit 130 is used to generate an output related to visual,auditory or tactile, and may include at least one of a display unit, anaudio output unit, a hap tip module, and an optical output unit. Thedisplay unit may form a layer structure or an integrated structure withthe touch sensor, thereby implementing a touch screen. Such a touchscreen may serve as a user input means for providing an input interfacebetween the augmented reality electronic device 10 and the user, and atthe same time, provide an output interface between the augmented realityelectronic device 10 and the user.

Input 120 and/or output 130 may also include an interface for connectingexternal peripherals such as Universal Serial Bus (USB), FireWire,Thunderbolt or other connection protocols. Input 120 and/or output 130may also include a network connection, such as an Ethernet port, modem,or the like. The voice output apparatus 10 may be connected to theInternet or a distributed computing environment through the input unit120 and/or the output unit 130. In addition, the voice output apparatus10 may be connected to a removable or external memory (eg, a removablememory card, a memory key drive, a network storage, etc.) through theinput unit 120 or the output unit 130.

The interface unit 160 serves as a path to various types of externaldevices connected to the electronic device 10. The electronic device 10may receive virtual reality or augmented reality content from anexternal device through the interface unit 160, and may interact witheach other by exchanging various input signals, sensing signals, anddata.

For example, the interface unit 160 may include a device equipped with awired/wireless headset port, an external charger port, a wired/wirelessdata port, a memory card port, and an identification module. It mayinclude at least one of a port connecting, an audio input/output (I/O)port, a video input/output (I/O) port, and an earphone port. The memory140 may store data and instructions. The memory 140 may include amagnetic storage, an optical storage, a solid-state storage type, andthe like.

The memory 140 may include volatile RAM, nonvolatile ROM, or anothertype of memory.

In addition, the memory 140 stores data supporting various functions ofthe electronic device 10. The memory 140 may store a plurality ofapplication programs or applications that are driven in the electronicdevice 10, data for operating the electronic device 10, andinstructions. At least some of these applications may be downloaded froman external server via wireless communication. At least some of theseapplication programs may be present on the electronic device 10 from thetime of shipment for the basic functions of the electronic device 10(for example, a call forwarding, a calling function, a messagereceiving, and a calling function).

The voice output apparatus 10 may include a processor 170. The processor170 may be connected to a bus (not shown), an input unit 120, an outputunit 130, and/or other components of the voice output device 10. Theprocessor 170 may correspond to a CPU for processing data, a computerreadable instruction for processing data, and a memory for storing dataand instructions.

In addition to the operations associated with the application, theprocessor 170 also typically controls the overall operation of theelectronic device 10. The processor 170 may process signals, data,information, and the like, which are input or output through theabove-described components.

In addition, the processor 170 may control at least some of thecomponents by driving an application program stored in the memory 140 toprovide appropriate information to the user or to process a function. Inaddition, the processor 170 may operate at least two or more of thecomponents included in the electronic device 10 in combination with eachother to drive an application program.

In addition, the processor 170 may detect the movement of the electronicdevice 10 or the user by using a gyroscope sensor, a gravity sensor, amotion sensor, or the like included in the sensing unit 150.Alternatively, the processor 170 may detect an object approaching to theelectronic device 10 or the user by using a proximity sensor, anillumination sensor, a magnetic sensor, an infrared sensor, anultrasonic sensor, an optical sensor, etc. included in the sensing unit150. In addition, the processor 170 may detect a user's movement throughsensors provided in a controller that operates in conjunction with theelectronic device 10.

In addition, the processor 170 may perform an operation (or function) ofthe electronic device 10 using an application program stored in thememory 140.

Computer instructions to be processed in the processor 170 for operatingthe voice output apparatus 10 and various components may be executed bythe processor 170 and may include a memory 140, an external device, or aprocessor to be described later. It may be stored in the memory orstorage included in (170). Alternatively, all or some of the executableinstructions may be embedded in hardware or firmware in addition tosoftware. One embodiment of the invention may be implemented in variouscombinations of software, firmware and/or hardware, for example.

In detail, the processor 170 may process the text data into an audiowaveform including voice, or may process the audio waveform into textdata. The source of the textual data may be generated by an internalcomponent of the voice output apparatus 10. In addition, the source ofthe text data may be received from the input unit such as a keyboard ortransmitted to the voice output apparatus 10 through a networkconnection. The text may be in the form of sentences that include text,numbers, and/or punctuation for conversion by the processor 170 intospeech. The input text may also include a special annotation forprocessing by the processor 170, and may indicate how the specific textshould be pronounced through the special annotation. The text data maybe processed in real time or later stored and processed.

In addition, although not shown in FIG. 6, the processor 170 may includea front end, a speech synthesis engine, and a TTS storage. Thepreprocessor may convert the input test data into a symbolic linguisticrepresentation for processing by the speech synthesis engine. The speechsynthesis engine may convert the input text into speech by comparingannotated phonetic units models with information stored in the TTSstorage. The preprocessor and the speech synthesis engine may include anembedded internal processor or memory, or may use the processor 170 andthe memory 140 included in the voice output apparatus 10. Instructionsfor operating the preprocessor and the speech synthesis engine may beincluded in the processor 170, the memory 140 of the voice outputapparatus 10, or an external device.

Text input to the processor 170 may be sent to the preprocessor forprocessing. The preprocessor may include a module for performing textnormalization, linguistic analysis, and linguistic prosody generation.

During the text normalization operation, the preprocessor processes thetext input and generates standard text, converting numbers,abbreviations, and symbols the same as they are written.

During the language analysis operation, the preprocessor may analyze thelanguage of the normalized text to generate a series of phonetic unitscorresponding to the input text. This process may be called phonetictranscription.

Phonetic units are finally combined to include symbolic representationsof sound units output by the voice output device 10 as speech. Varioussound units can be used to segment text for speech synthesis.

Processor 170 includes phonemes (individual sounds), half-phonemes,di-phones (the last half of a phoneme combined with the first half ofadjacent phonemes) and bi-phones. Speech can be processed based on twosuccessive sonic speeds, syllables, words, phrases, sentences, or otherunits. Each word may be mapped to one or more phonetic units. Suchmapping may be performed using a language dictionary stored in the voiceoutput apparatus 10.

Linguistic analysis performed by the preprocessor may also involveidentifying different grammatical elements, such as prefixes, suffixes,phrases, punctuation, and syntactic boundaries. Such grammaticalcomponents can be used by the processor 170 to produce natural audiowaveform output. The language dictionary may also includeletter-to-sound rules and other tools that may be used to pronouncepreviously unverified words or letter combinations that may be generatedby the processor 170. In general, the more information included in thelanguage dictionary, the higher the quality of voice output can beguaranteed.

Based on the language analysis, the preprocessor may perform languagerhythm generation annotated to phonetic units with prosodiccharacteristics indicating how the final sound unit should be pronouncedin the final output speech.

The rhyme characteristics may also be referred to as acoustic features.During the operation of this step, the preprocessor may integrate intothe processor 170 taking into account any prosodic annotations involvingtext input. Such acoustic features may include pitch, energy, duration,and the like. Application of the acoustic feature may be based onprosodic models available to the processor 170.

This rhyme model represents how phonetic units should be pronounced incertain situations. For example, a rhyme model can include a phoneme'sposition in a syllable, a syllable's position in a word, or a word'sposition in a sentence or phrase, phrases neighboring phonetic units,and the like. Like the language dictionary, the more information of theprosodic model, the higher the quality of voice output can beguaranteed.

The output of the preprocessor may include a series of speech unitsannotated with prosodic characteristics. The output of the preprocessormay be referred to as a symbolic linguistic representation. The symboliclanguage representation may be sent to a speech synthesis engine.

The speech synthesis engine performs a process of converting a speechinto an audio waveform for output to the user through the output unit130. The speech synthesis engine may be configured to convert the inputtext into high quality natural speech in an efficient manner. Such highquality speech can be configured to pronounce as much as possible ahuman speaker.

The speech synthesis engine may perform speech synthesis using at leastone other method.

The Unit Selection Engine contrasts a recorded speech database with asymbolic linguistic representation generated by the preprocessor. Theunit selection engine matches the symbol language representation withthe speech audio unit of the speech database. Matching units can beselected to form a speech output and the selected matching units can beconnected together. Each unit has only an audio waveform correspondingto a phonetic unit, such as a short .wav file of a particular sound,with a description of the various acoustic characteristics associatedwith the .wav file (pitch, energy, etc.). In addition, the speech unitmay include other information such as a word, a sentence or a phrase, alocation displayed on a neighboring speech unit.

The unit selection engine can match the input text using all theinformation in the unit database to produce a natural waveform. The unitdatabase may include an example of a number of speech units that providedifferent options to the voice output device 10 for connecting the unitsin speech. One of the advantages of unit selection is that natural voiceoutput can be produced depending on the size of the database. Inaddition, as the unit database is larger, the voice output apparatus 10may configure natural speech.

On the other hand, there is a parameter synthesis method in addition tothe above-described unit selection synthesis. Parametric synthesisallows synthesis parameters such as frequency, volume and noise to bemodified by a parametric synthesis engine, a digital signal processor,or other audio generating device to produce an artificial speechwaveform.

Parametric synthesis can be matched to a desired linguisticrepresentation desired output speech parameter using an acoustic modeland various statistical techniques. Parameter synthesis not onlyprocesses speech without the large database associated with unitselection, but also enables accurate processing at high processingspeeds. The unit selection synthesis method and the parameter synthesismethod may be performed separately or in combination to generate a voiceaudio output.

Parametric speech synthesis may be performed as follows. The processor170 may include an acoustic model capable of converting a symboliclinguistic representation into a synthetic acoustic waveform of the textinput based on the audio signal manipulation. The acoustic model mayinclude rules that may be used by the parameter synthesis engine toassign specific audio waveform parameters to input speech units and/orprosodic annotations. The rule can be used to calculate a scoreindicating the likelihood that a particular audio output parameter(frequency, volume, etc.) corresponds to the portion of the inputsymbolic language representation from the preprocessor.

The parametric synthesis engine may apply a plurality of techniques tomatch the voice to be synthesized with the input speech unit and/or therhyme annotation. One common technique uses the Hidden Markov Model(HMM), which can be used to determine the probability that an audiooutput should match text input. The HMM can be used to convert theparameters of language and acoustic space into parameters to be used bya vocoder (digital voice encoder) to artificially synthesize the desiredspeech.

The voice output apparatus 10 may also include a speech unit databasefor use in unit selection. The voice unit database may be stored inmemory 140 or other storage configuration. The voice unit database mayinclude recorded speech utterance. The speech utterance may be textcorresponding to the speech content. In addition, the speech unitdatabase may include recorded speech (in the form of audio waveforms,feature vectors or other formats) that takes up significant storagespace in the voice output device 10. Unit samples of the speech unitdatabase may be classified in a variety of ways, including speech units(phonemes, diphonies, words, etc.), linguistic rhyme labels, soundfeature sequences, speaker identities, and the like. Sample utterancecan be used to generate a mathematical model corresponding to thedesired audio output for a particular speech unit.

When the speech synthesis engine matches the symbolic languagerepresentation, it may select a unit in the speech unit database thatmost closely matches the input text (including both speech units andrhythm annotations). In general, the larger the voice unit database, thegreater the number of selectable unit samples, thus enabling accuratespeech output.

The processor 170 may transmit audio waveforms including audio output tothe output unit 130 for output to the user. The processor 170 may storethe audio waveform including speech in the memory 140 in a plurality ofdifferent formats, such as a series of feature vectors, uncompressedaudio data, or compressed audio data. For example, processor 170 mayencode and/or compress voice output using an encoder/decoder prior tothe transmission. The encoder/decoder may encode and decode audio datasuch as digitized audio data, feature vectors, and the like. Inaddition, the functions of the encoder/decoder may be located inseparate components or may be performed by the processor 170.

The memory 140 may store other information for voice output. Thecontents of memory 140 may be prepared for general voice output and TTSuse, and may be customized to include sounds and words that are likelyto be used in a particular application. For example, the TTS storage mayinclude customized voice specialized for location and navigation for TTSprocessing by the GPS device.

The memory 140 may also be customized to the user based on thepersonalized desired voice output. For example, a user may prefer avoice that is output to a specific gender, a specific intonation, aspecific speed, a specific emotion (eg, a happy voice). The speechsynthesis engine may include a specialized database or model to describesuch user preferences.

The voice output apparatus 10 may also be configured to perform TTSprocessing in multiple languages. For each language, processor 170 mayinclude data, instructions, and/or components specifically configured tosynthesize speech in a desired language.

The processor 170 may modify or update the contents of the memory 140based on the feedback on the result of the TTS processing to improveperformance, so that the processor 170 may improve awareness of thevoice more than the capability provided by the training corpus.

As the processing power of the voice output apparatus 10 is improved,the speech output may be performed by reflecting the emotion attributeof the input text. Alternatively, even if the input text is not includedin the emotion attribute, the voice output apparatus 10 may output thevoice by reflecting the intention (emotional information) of the userwho created the input text.

Indeed, when a model is built that will be integrated into a TTS modulethat performs TTS processing, the TTS system may integrate the variouscomponents mentioned above with other components. For example, the voiceoutput apparatus 10 may include a block for setting a speaker.

The speaker setting unit may set the speaker for each characterappearing in the script. The speaker setup unit may be integrated intothe processor 170 or may be integrated as part of the preprocessor orspeech synthesis engine. The speaker setting unit synthesizes textcorresponding to a plurality of characters into a set speaker's voiceusing metadata corresponding to a speaker profile.

According to an embodiment of the present invention, a markup languagemay be used as the meta data, and preferably, speech synthesis markuplanguage (SSML) may be used.

The power supply unit 190 receives power from an external power sourceor an internal power source under the control of the processor 170 tosupply power to each component included in the electronic device 10. Thepower supply unit 190 includes a battery, and the battery may beprovided in a built-in or replaceable form.

Hereinafter, a speech processing procedure performed by a deviceenvironment and/or a cloud environment or server environment will bedescribed with reference to FIGS. 7 and 8. FIG. 7 shows an example inwhich, while a speech can be received in a device 50, a procedure ofprocessing the received speech and thereby synthesize the speech, thatis, overall operations of speech synthesis, is performed in a cloudenvironment 60. On the contrary, FIG. 8 shows an example of on-deviceprocessing indicating that a device 70 performs the aforementionedoverall operations of speech synthesis by processing a received speechand thereby synthesizing the speech.

In FIGS. 7 and 8, the device environments 70 may be referred to asclient devices, and the cloud environments 60 and 80 may be referred toas servers.

FIG. 7 shows a schematic block diagram of a text-to-speech (TTS) devicein a TTS system according to an embodiment of the present invention.

In order to process a speech event in an end-to-end speech UIenvironment, various configurations are required. A sequence forprocessing the speech event performs signal acquisition playback, speechpre-processing, voice activation, voice output, natural languageprocessing, and speech synthesis by which a device responds to a user.

The client device 50 may include an input module. The input module mayreceive a user input from a user. For example, the input module mayreceive the user input from an external device (e.g., a keyboard and aheadset) connected thereto. In addition, for example, the input modulemay include a touch screen. In addition, for example, the input modulemay include a hardware key located in a user terminal.

According to an embodiment, the input module may include at least onemicrophone capable of receiving a user's utterance as a speech signal.The input module may include a speech input system and receive a user'sspeech as a speech signal through the speech input system. By generatingan input signal for an audio input, the at least one microphone maydetermine a digital input signal for a user's speech. According to anembodiment, multiple microphones may be implemented as an array. Thearray may be arranged in a geometric pattern, for example, a lineargeometric shape, a circular geometric shape, or a different randomshape. For example, the array may be in a pattern in which four sensorsare placed at 90 degrees to receive sound from four directions. In someembodiments, the microphone may include sensors of different arrays in aspace of data communication, and may include a networked array of thesensors. The microphone may include an omnidirectional microphone and adirectional microphone (e.g., a shotgun microphone).

The client device 50 may include a pre-processing module 51 capable ofpre-processing a user input (speech signal) that is received through theinput module (e.g., a microphone).

The pre-processing module 51 may include an adaptive echo canceller(AEC) function to thereby remove echo included in a user speech signalreceived through the microphone. The pre-processing module 51 mayinclude a noise suppression (NS) function to thereby remove backgroundnoise included in a user input. The pre-processing module 51 may includean end-point detect (EPD) function to thereby detect an end point of auser speech and thus find out where the user speech exists. In addition,the pre-processing module 51 may include an automatic gain control (AGC)function to thereby control volume of the user speech in such a waysuitable for recognizing and processing the user speech.

The client device 50 may include a voice activation module 52. The voiceactivation module 52 may recognize a wake-up call indicative ofrecognition of a user's call. The voice activation module 52 may detecta predetermined keyword (e.g., Hi LG) from a user input which has beenpre-processed. The voice activation module 52 may remain in a standbystate to perform an always-on keyword detection function.

The client device 50 may transmit a user voice input to a cloud server.ASR and natural language understanding (NLU) operations, which areessential to process a user speech, is generally performed in Cloud dueto computing, storage, power limitations, and the like. The Cloud mayinclude the cloud device 60 that processes a user input transmitted froma client. The cloud device 60 may exists as a server.

The cloud device 60 may include an auto voice output (ASR) module 61, anartificial intelligent agent 62, a natural language understanding (NLU)module 63, a text-to-speech (TTS) module 64, and a service manager 65.

The ASR module 61 may convert a user input, received from the clientdevice 50, into textual data.

The ASR module 61 includes a front-end speech pre-processor. Thefront-end speech pre-processor extracts a representative feature from aspeech input. For example, the front-perform a Fourier transform on thespeech input to extract spectral features that characterize the speechinput as a sequence of representative multi-dimensional vectors. Inaddition, The ASR module 61 may include one or more voice output modules(e.g., an acoustic model and/or a language module) and may realize oneor more voice output engines. Examples of the voice output model includeHidden Markov Models, Gaussian-Mixture Models, Deep Neural NetworkModels, n-gram language models, and other statistical models. Examplesof the voice output model include a dynamic time warping (DTW)-basedengine and a weighted finite state transducer (WFST)-based engine. Theone or more voice output models and the one or more voice output enginescan be used to process the extracted representative features of thefront-end speech pre-processor to produce intermediate recognitionsresults (e.g., phonemes, phonemic strings, and sub-words), andultimately, text recognition results (e.g., words, word strings, orsequence of tokens).

Once the ASR module 61 generates a recognition result including a textstring (e.g., words, or sequence of words, or sequence of tokens), therecognition result is transmitted to the NLP module 732 for intentiondeduction. In some examples, The ASR module 730 generates multiplecandidate text expressions for a speech input. Each candidate textexpression is a sequence of works or tokens corresponding to the speechinput.

The NLU module 63 may perform a syntactic analysis or a semanticanalysis to determine intent of a user. The syntactic analysis may beused to divide a user input into syntactic units (e.g., words, phrases,morphemes, or the like) and determine whether each divided unit has anysyntactic element. The semantic analysis may be performed using semanticmatching, rule matching, formula matching, or the like. Thus, the NLUmodule 63 may obtain a domain, intent, or a parameter (or a slot)necessary to express the intent from a user input through theabove-mentioned analysis.

According to an embodiment, the NLU module 63 may determine the intentof the user and a parameter using a matching rule which is divided intoa domain, intent, and a parameter. For example, one domain (e.g., analarm) may include a plurality of intents (e.g., alarm setting, alarmrelease, and the like), and one intent may need a plurality ofparameters (e.g., a time, the number of iterations, an alarm sound, andthe like). The plurality of rules may include, for example, one or moremandatory parameters. The matching rule may be stored in a naturallanguage understanding database.

According to an embodiment, the NLU module 63 may determine a meaning ofa word extracted from a user input using a linguistic feature (e.g., asyntactic element) such as a morpheme or a phrase and may match thedetermined meaning of the word to the domain and intent to determine theintent of the user. For example, the NLU module 63 may determine theintent of the user by calculating how many words extracted from a userinput are included in each of the domain and the intent. According to anembodiment, the NLU module 63 may determine a parameter of the userinput using a word which is the basis for determining the intent.According to an embodiment, the NLU module 63 may determine the intentof the user using a NLU DB which stores the linguistic feature fordetermining the intent of the user input. According to anotherembodiment, the NLU module 63 may determine the intent of the user usinga personal language model (PLM). For example, the NLU module 63 maydetermine the intent of the user using personalized information (e.g., acontact list, a music list, schedule information, social networkinformation, etc.). For example, the PLM may be stored in, for example,the NLU DB. According to an embodiment, the ASR module 61 as well as theNLU module 63 may recognize a voice of the user with reference to thePLM stored in the NLU DB.

According to an embodiment, the NLU module 63 may further include anatural language generating module (not shown). The natural languagegenerating module may change specified information to a text form. Theinformation changed to the text form may be a natural language speech.For example, the specified information may be information about anadditional input, information for guiding the completion of an actioncorresponding to the user input, or information for guiding theadditional input of the user. The information changed to the text formmay be displayed in a display after being transmitted to the clientdevice or may be changed to a voice form after being transmitted to theTTS module.

The TTS module 64 may convert text input to voice output. The TTS module64 may receive text input from the NLU module 63 of the LNU module 63,may change the text input to information in a voice form, and maytransmit the information in the voice form to the client device 50. Theclient device 50 may output the information in the voice form via thespeaker.

The speech synthesis module 64 synthesizes speech outputs based on aprovided text. For example, a result generated by the ASR module 61 maybe in the form of a text string. The speech synthesis module 64 mayconvert the text string to an audible speech output. The speechsynthesis module 64 may use any appropriate speech synthesis techniquein order to generate speech outputs from text, including, but notlimited, to concatenative synthesis, unit selection synthesis, diphonesynthesis, domain-specific synthesis, formant synthesis, articulatorysynthesis, hidden Markov model (HMM) based synthesis, and sinewavesynthesis.

In some examples, the speech synthesis module 64 may be configured tosynthesize individual words based on phonemic strings corresponding tothe words. For example, a phonemic string can be associated with a wordin a generated text string. The phonemic string can be stored inmetadata associated with the word. The speech synthesis model 64 may beconfigured to directly process the phonemic string in the metadata tosynthesize the word in speech form.

Since the cloud environment generally has more processing capabilitiesor resources than the client device, a higher quality speech output maybe obtained in synthesis on the client side. However, the presentinvention is not limited thereto, and the speech synthesis process maybe performed on the client side (see FIG. 8).

Meanwhile, according to an embodiment, the client environment mayfurther include an Artificial Intelligence (AI) agent 62. The AIprocessor 62 is defined to perform at least some of the above-describedfunctions performed by the ASR module 61, the NLU module 62 and/or theTTS module 64. In addition, the AI module 62 may make contribution sothat the ASR module 61, the NLU module 62 and/or the TTS module 64perform independent functions, respectively.

The AI processor module 62 may perform the above-described functionsthrough deep learning. The deep learning represents a certain data in aform readable by a computer (e.g., when the data is an image, pixelinformation is represented as column vectors or the like), and effortsare being made to conduct enormous researches for applying therepresentation to learning (which is about how to create betterrepresentation techniques and how to create a model that learns thebetter representation techniques), and, as a result, various deeplearning techniques such as deep neural networks (DNN), convolutionaldeep neural networks (CNN), Recurrent Boltzmann Machine (RNN),Restricted Boltzmann Machine (RBM), deep belief networks (DBN), and DeepQ-Network, may be applied to computer vision, voice output, naturallanguage processing, speech/signal processing, and the like.

Currently, all commercial voice output systems (Microsoft's Cortana,Skype translator, Google Now, Apple Ski, etc.). are based on deeplearning techniques.

In particular, the AI processor module 62 may perform various naturallanguage processes, including machine translation, emotion analysis, andinformation retrieval, to process natural language by use of a deepartificial neural network architecture.

Meanwhile, the cloud environment may include a service manager 65capable of collecting various personalized information and supporting afunction of the AI processor 62. The personalized information obtainedthrough the service manager may include at least one data (a calendarapplication, a messaging service, usage of a music application, etc.)used through the cloud environment, at least one sensing data (a camera,a microphone, temperature, humidity, a gyro sensor, C-V2X, a pulse,ambient light, Iris scan, etc.) collected by the client device 50 and/orthe cloud 60, off device data directly not related to the client device50. For example, the personalized information may include maps, SMS,news, music, stock, weather, Wikipedia information.

For convenience of explanation, the AI processor 62 is represented as anadditional block to be distinguishable from the ASR module 61, the NLUmodule 63, and the TTS module 64, but the AI processor 62 may perform atleast some or all of the functions of the respective modules 61, 62, and64.

In FIG. 7, an example in which the AI processor 62 is implemented in thecloud environment due to computing calculation, storage, powerlimitations, and the like, but the present invention is not limitedthereto.

For example, FIG. 8 is identical to what is shown in FIG. 7, except fora case where the AI processor is included in the cloud device.

FIG. 8 is a schematic block diagram of a TTS device in a TTS systemenvironment according to an embodiment of the present invention. Aclient device 70 and a cloud environment 80 shown in FIG. 8 maycorrespond to the client device 50 and the cloud device 60aforementioned in FIG. 7, except for some configurations and functions.Accordingly, description about specific functions of correspondingblocks may refer to FIG. 7.

Referring to FIG. 8, the client device 70 may include a pre-processingmodule 51, a voice activation module 72, an ASR module 73, an AIprocessor 74, an NLU module 75, and a TTS module 76. In addition, theclient device 50 may include an input module (at least one microphone)and at least one output module.

In addition, the cloud environment may include cloud knowledge 80 thatstores personalized information in a knowledge form.

A function of each module shown in FIG. 8 may refer to FIG. 7. However,since the ASR module 73, the NLU module 75, and the TTS module 76 areincluded in the client device 70, communication with Cloud may not benecessary for a speech processing procedure such as voice output, speechsynthesis, and the like, and thus, an instant real-time speechprocessing operation is possible.

Each module shown in FIGS. 7 and 8 are merely an example for explaininga speech processing procedure, and modules more or less than in FIGS. 7and 8 may be included. In addition, two or more modules may be combinedor different modules or modules with different arrangement structuresmay be included. The various modules shown in FIGS. 7 and 8 may beimplemented in hardware, software instructions for execution by one ormore processors, firmware, including one or more signal processingand/or application specific integrated circuits, or a combinationthereof.

FIG. 9 is a schematic block diagram of an AI processor capable ofperforming emotion classification information-based TTS according to anembodiment of the present invention.

Referring to FIG. 9, in the speech processing procedure described withreference to FIGS. 7 and 8, the AI processor 74 may support aninteractive operation with a user, in addition to an ASR operation, anNLU operation, and a TTS operation. Alternatively, using contextinformation, the AI processor 74 may make contribution so that the NLUmodule 63 further clarify, complements, or additionally defineinformation included in text expressions received from the ASR module61.

Here, the context information may include preference of a user of aclient device, hardware and/or software states of the client device,various types of sensor information received before, during, or after auser input, previous interactions (e.g., dialogue) between the AIprocessor and the user, etc. In the present disclosure, the contextinformation is dynamic and varies depending on time, location, contentsof the dialogue, and other elements.

The AI processor 74 may further include a context fusion and learningmodule 91, a local knowledge 92, and a dialogue management 93.

The context fusion and learning module 91 may learn a user's intentbased on at least one data. The at least one data may further include atleast one sensing data obtained by a client device or a cloudenvironment. In addition, the at least one data may further includespeaker identification, acoustic event detection, a speaker's personalinformation (gender and age detection), voice activity detection (VAD),and emotion classification information.

The speaker identification may indicate specifying a speaker in aspeaker group registered by a speech. The speaker identification mayinclude identifying a pre-registered speaker or registering a newspeaker. The acoustic event detection may outdo a voice output techniqueand may be used to recognize acoustics itself to recognize a type ofsound and a place where the sound occurs. The VAD is a speech processingtechnique of detecting presence or absence of a human speech (voice)from an audio signal that can include music, noise, or any other sound.According to an embodiment, the AI processor 74 may detect presence of aspeech from the input audio signal. According to an embodiment the AIprocessor 74 differentiates a speech data and a non-speech data using adeep neural networks (DNN) model. In addition, the AI processor 74 mayperform emotion classification information on the speech data using theDNN model. According to the emotion classification information, thespeech data may be classified as anger, boredom, fear, happiness, orsadness.

The contest fusion and learning module 91 may include a DNN model toperform the above-described operation, and may determine intent of auser input based on sensing information collected in the DNN model, theclient device or the cloud environment.

The at least one data is merely an example and may include any data thatcan be referred to so as to determine intent of a user in a speechprocessing procedure. The at least one data may be obtained through theabove-described DNN model.

The AI processor 74 may include the local knowledge 92. The localknowledge 92 may include user data. The user data may include a user'spreference, the user's address, the user's initially set language, theuser's contact list, etc. According to an embodiment, the AI processor74 may additionally define the user's intent by complementinginformation included in the user's speech input using the user'sspecific information. For example, in response to the user's request“Invite my friends to my birthday party”, the AI processor 74 does notrequest more clarified information from the user and may utilize thelocal knowledge 92 to determine who “the friends” are and when and wherethe “birthday” takes place.

The AI processor 74 may further include the dialogue management 93. TheAI processor 74 may provide a dialogue interface to enable speechconversation with the user. The dialogue interface may refer to aprocedure of outputting a response to the user's speech input through adisplay or a speaker. Here, a final result output through the dialogueinterface may be based on the ASR operation, the NLU operation, and theTTS operation, which are described above.

I. Speech Recognition Method

FIG. 10 is a flowchart illustrating a voice recognizing method accordingto an embodiment of the present invention.

Referring to FIG. 10, a processor 170 of a voice recognizing device 10may obtain a microphone detection signal via at least one microphone(e.g., the input unit 120) (S110).

The microphone detection signal may include signal information aboutdetection of an external signal obtained via the at least one microphone120.

The microphone detection signal may include a user's utterance.

Subsequently, the processor 170 may recognize the user's voice from themicrophone detection signal based on a pre-learned speech recognitionmodel (S130).

For example, the speech recognition model may previously be learned tooutput a result of recognizing the user's voice using the microphonedetection signal as an input. For example, the result of recognition ofthe user's voice may include information related to whether the user'sspeech recognition has succeeded, information related to the accuracy ofrecognition of the user's voice, text generated by recognizing theuser's voice and output via the output unit 130, an image generated byrecognizing the user's voice and output via the output unit 130, a soundgenerated by recognizing the user's voice and output via the output unit130, and text resulting from recognizing the user's voice multiple timesand modifying the first recognition result with the subsequentrecognition results.

Then, the processor 170 may output information related to the result ofrecognition of the user's voice (S150).

For example, the output unit 130 for outputting the result ofrecognition of the user's voice may include a display or a speaker.

Last, the processor 170 may update (tune) the speech recognition model(pre-processing model) based on the speech recognition resultinformation output via the output unit 130 (S170).

The speech recognition model may be stored in at least one of theprocessor 170, the AI processor 21, the deep learning model 26, the AIprocessor 261, and/or the memory 140. The processor 170 may analyze thespeech recognition result information output via the output unit 130 andtune the parameters of the speech recognition model based on a trainingset of the speech recognition result information and voice, therebyupdating the speech recognition model.

FIG. 11 illustrates a process of updating a speech recognition modelaccording to an embodiment of the present invention.

Referring to FIG. 11, the processor 170 may include a speech recognitionmodel 171. The speech recognition model 171 is an example of artificialneural network (ANN).

The processor 170 may input a microphone detection signal 121 to thespeech recognition model 171, pre-process the microphone detectionsignal based on the speech recognition model 171, and then transmit thepre-processed speech recognition result to the output unit 130.

The speech recognition model 171 may previously be learned to output thespeech recognition result using the microphone detection signal 121 asan input.

The output unit 130 may output the speech recognition result information131 under the control of the processor 170.

The speech recognition result information 131 may be set as a reward inthe deep learning technology for tuning the parameter 172 of the speechrecognition model 171. In other words, the processor 170 may tune(update) the parameter 172 of the speech recognition model 171 based onthe speech recognition result information 131 output via the output unit130.

FIG. 12 illustrates information related to whether speech recognitionsucceeds as an example of speech recognition result information.

Referring to FIG. 12, the speech recognition result information mayinclude information 1210 and 1211 related to whether the recognition ofthe user's voice has succeeded in the microphone detection signal.

For example, as shown in FIG. 12, when the microphone detection signal1201 is obtained, the processor 170 may attempt speech recognition forthe microphone detection signal 1201 based on the speech recognitionmodel.

The processor 170 may output, via the output unit 130, the speechrecognition attempt result, e.g., text 1210 (e.g., text generated byspeech recognition) (“result:”) or the speech recognition attemptresult, e.g., information 1211 related to word count (e.g., recognizedword count) (“recognized word count: 0).

The processor 170 may update (tune) the speech recognition model basedon the speech recognition attempt result, e.g., text 1210 (e.g., textgenerated by speech recognition) (”result:“) or the speech recognitionattempt result, e.g., information 1211 related to word count (e.g.,recognized word count) (”recognized word count: 0).

For example, the processor 170 may update (tune) the parameters of thespeech recognition model with the parameter which enables more text1210, which is the result of speech recognition attempt, to be outputvia the output unit 130 based on the voice and the text 1210, which isthe result of speech recognition attempt.

For example, the processor 170 may update (tune) the parameters of thespeech recognition model with the parameter which enables the wordcount-related information 1211, which is the result of speechrecognition attempt, to have a larger value based on voice and the wordcount-related information 1211, which is the result of speechrecognition attempt.

FIG. 13 illustrates information related to a post-processing result of aspeech recognition result as another example of the speech recognitionresult information.

Referring to FIG. 13, the speech recognition result information mayinclude the results 1310, 1320, 1321, 1330, 1340, and 1341 ofpost-processing of the information 1210 and 1211 related to whether thespeech recognition of FIG. 12 has succeeded.

For example, as shown in FIG. 13, when the microphone detection signal1301 is obtained, the processor 170 may attempt speech recognition forthe microphone detection signal 1301 based on the speech recognitionmodel.

The processor 170 may perform post-processing on the information (text)1310 related to whether speech recognition has succeeded. For example,the post-processing may mean the process of attempting speechrecognition on the text 1310 multiple times and repeatedly modifying thetext 1310, which is the result of the first speech recognition attempt,based on the multiple speech recognition attempts.

For example, the processor 170 may repeatedly perform modification onthe information (text) 1310 (“result: How is the weather in Seoulumbrella today”) related to whether speech recognition has succeeded tothereby modify it to “result: How is the weather in Seoul”) and outputthe modified text 1320 and modification result (“result: modificationcount: 3”) 1321 via the output unit 130.

The processor 170 may update (tune) the speech recognition model basedon the modified text 1320 and the modification result (“result:modification count: 3”) 1321.

For example, the processor 170 may update (tune) the parameters of thespeech recognition model with the parameter, which enables lessmodification results to be displayed (1330, 1340, and 1341), based onthe voice and the modified text 1320 and modification result 1321.

FIG. 14 illustrates information related to a speech recognition time asstill another example of the speech recognition result information.

Referring to FIG. 14, a processor 170 may recognize the microphonedetection signal 1401 “How is the weather in Seoul today” based on thespeech recognition model.

As a result of attempting speech recognition on the microphone detectionsignal 1401 based on the speech recognition model, the processor 170 mayoutput, via the output unit 130, information 1411 (“screen displayspeed: 700 ms”) related to the time that the text 1410 generated viaspeech recognition is recognized.

The processor 170 may tune (update) the parameters of the speechrecognition model with the parameter which enables the speechrecognition time-related information 1411 has a smaller value.

FIG. 15 is a flowchart illustrating a process of performing the speechrecognition model update of FIG. 10 via AI processing.

Referring to FIG. 16, the processor 170 may update a speech recognitionmodel in steps S170 and S171 which are described below in detail.

First, the processor 170 may extract a feature value from speechrecognition result information (S171).

For example, the processor 170 may perform pre-processing on theimage-type speech recognition result information output via the outputunit 130 and extract the feature value (feature vector) of thepre-processed speech recognition result information.

Subsequently, the processor 170 may update a pre-learned speechrecognition model based on the extracted speech recognition resultinformation feature value and the voice (S172).

For example, the processor 170 may set a training set which has theextracted speech recognition result information feature value as anoutput value and the voice as an input value and update the speechrecognition model based on the training set. In other words, theprocessor 170 may tune the speech recognition model for pre-processingbased on the training set.

FIG. 16 is a flowchart illustrating a process of performing the modelupdate (S172) of FIG. 15 via a 5G network.

First, the voice recognizing device 10 or the processor 170 of the voicerecognizing device may control the communication unit 110 to transmit afeature value extracted from obtained speech recognition resultinformation to an AI processor included in a 5G network. The processor170 may control the communication unit to receive the AI-processedinformation from the AI processor.

The AI-processed information may include parameters of a speechrecognition model updated based on the speech recognition resultinformation.

The processor 170 may perform an initial access procedure with the 5Gnetwork to transmit the speech recognition result information to the 5Gnetwork. The processor 170 may perform the initial access procedure withthe 5G network based on a synchronization signal block (SSB).

The processor 170 may receive, from a network through the wirelesscommunication unit, downlink control information (DCI) used forscheduling transmission of the speech recognition result information.

The processor 170 may transmit the speech recognition result informationto the network based on the DCI.

The processor 170 may transmit the speech recognition result informationto the network via a PUSCH, and the SSB and the DM-RS of the PUSCH maybe QCLed for QCL type D.

Subsequently, as shown in FIG. 16, the processor 170 may transmit thevoice and the feature value extracted from the speech recognition resultinformation to the 5G network (S1721).

Subsequently, the 5G network may perform AI processing (1722) based onthe voice and the feature value extracted from the speech recognitionresult information which is described below in detail.

First, the AI processor included in the 5G network may train the speechrecognition model with the voice and the feature value extracted fromthe speech recognition result information as a first step of the AIprocessing 1722 (S1723).

Subsequently, the AI processor may obtain the updated speech recognitionmodel parameter as the output value of the speech recognition model(S1724).

The 5G network may transmit the updated speech recognition modelparameter to the voice recognizing device (S1725).

Unlike shown in FIG. 16, the voice recognizing device may transmit onlythe voice and speech recognition result information to the 5G network,and the AI processor of the 5G network may extract the feature value forupdating the speech recognition model from the speech recognition resultinformation.

The AI processor of the 5G network may update the speech recognitionmodel based on the voice and the feature value extracted from the speechrecognition result information and transmit the whole speech recognitionmodel to the voice recognizing device.

J. Summary of Embodiments

Embodiment 1: An intelligent voice recognizing method comprises:obtaining a microphone detection signal; recognizing a user's voice fromthe microphone detection signal based on a pre-learned speechrecognition model; and generating information related to a result ofrecognition of the user's voice, wherein the speech recognition model isupdated based on the generated speech recognition result information.

Embodiment 2: In embodiment 1, the speech recognition result informationincludes information related to whether the speech recognition succeeds.

Embodiment 3: In embodiment 2, the information related to whether thespeech recognition succeeds includes text information generated byrecognizing the voice.

Embodiment 4: In embodiment 2, the information related to whether thespeech recognition succeeds includes information related to the numberof words included in the text information.

Embodiment 5: In embodiment 1, the method further comprises performingpost-processing on the generated speech recognition result information,wherein the speech recognition result information includes informationrelated to a result of the post-processing.

Embodiment 6: In embodiment 5, the information related to the result ofthe post-processing includes information related to a number of times inwhich the text information generated by the post-processing is modified.

Embodiment 7: In embodiment 1, the speech recognition result informationincludes information related to a speed at which the speech recognitionresult information is generated.

Embodiment 8: In embodiment 1, the method further comprises receiving,from a network, downlink control information (DCI) used for schedulingtransmission of the speech recognition result information andtransmitting the speech recognition result information to the networkbased on the DCI.

Embodiment 9: In embodiment 8, the method comprises performing aninitial access procedure with the network based on a synchronizationsignal block (SSB), and transmitting the speech recognition resultinformation to the network via a physical uplink shared channel (PUSCH),wherein demodulation-reference signals (DM-RSs) of the SSB and the PUSCHare quasi co-located (QCL) for QCL type D.

Embodiment 10: In embodiment 8, the method further comprises controllinga communication unit to transmit the speech recognition resultinformation to an artificial intelligence (AI) processor included in thenetwork and controlling the communication unit to receive AI-processedinformation from the AI processor, wherein the AI-processed informationincludes a parameter of the speech recognition model updated based onthe speech recognition result information.

Embodiment 11: An intelligent voice recognizing device comprises atleast one microphone detecting an external signal and a processorrecognizing a user's voice from a microphone detection signal obtainedvia the at least one microphone based on a pre-learned speechrecognition model and generating information related to a result ofrecognition of the user's voice, wherein the speech recognition model isupdated based on the generated speech recognition result information.

Embodiment 12: In embodiment 11, the speech recognition resultinformation includes information related to whether the speechrecognition succeeds.

Embodiment 13: In embodiment 12, the information related to whether thespeech recognition succeeds includes text information generated byrecognizing the voice.

Embodiment 14: In embodiment 12, the information related to whether thespeech recognition succeeds includes information related to the numberof words included in the text information.

Embodiment 15: In embodiment 11, the processor performs post-processingon the generated speech recognition result information, wherein thespeech recognition result information includes information related to aresult of the post-processing.

Embodiment 16: In embodiment 15, the information related to the resultof the post-processing includes information related to a number of timesin which the text information generated by the post-processing ismodified.

Embodiment 17: In embodiment 11, the speech recognition resultinformation includes information related to a speed at which the speechrecognition result information is generated.

Embodiment 18: In embodiment 11, the voice recognizing device furthercomprises a communication unit for performing wireless communicationwith an external device, wherein the processor receives, from a networkthrough the communication unit, downlink control information (DCI) usedfor scheduling transmission of the speech recognition result informationand transmits the speech recognition result information through thecommunication unit to the network based on the DCI.

Embodiment 19: In embodiment 18, the processor performs an initialaccess procedure with the network based on a synchronization signalblock (SSB) through the communication unit and transmits, through thecommunication unit to the network, the speech recognition resultinformation via a PUSCH, and wherein demodulation-reference signals(DM-RSs) of the SSB and the PUSCH are quasi co-located (QCL) for QCLtype D.

Embodiment 20: In embodiment 18, the processor controls thecommunication unit to transmit the speech recognition result informationto an artificial intelligence (AI) processor included in the network andcontrols the communication unit to receive AI-processed information fromthe AI processor, wherein the AI-processed information includes aparameter of the speech recognition model updated based on the speechrecognition result information.

Embodiment 21: There is provided a non-transitory computer-readablemedium storing a computer-executable component configured to be executedby one or more processors of a computing device, the computer-executablecomponent comprising obtaining a microphone detection signal,recognizing a user's voice from a microphone detection signal obtainedvia the at least one microphone based on a pre-learned speechrecognition model, and generating information related to a result ofrecognition of the user's voice, wherein the speech recognition model isupdated based on the generated speech recognition result information.

The effects of the intelligent voice output method, voice outputapparatus and intelligent computing device according to an embodiment ofthe present invention are as follows.

According to the present invention, the intelligent voice recognizingdevice may easily update a speech recognition model for speechrecognition based on speech recognition result information which isintuitively shown to the user.

According to the present invention, the intelligent voice recognizingmethod may allow a voice recognizing device to easily tune parameters ofa speech recognition model based on output information of the voicerecognizing device even when the manufacturer of the voice recognizingdevice differs from the manufacturer of the speech recognition model.

Effects which can be achieved by the present invention are not limitedto the above-mentioned effects. That is, other objects that are notmentioned may be obviously understood by those skilled in the art towhich the present invention pertains from the following description.

The above-described invention may be implemented in computer-readablecode in program-recorded media. The computer-readable media include alltypes of recording devices storing data readable by a computer system.Example computer-readable media may include hard disk drives (HDDs),solid state disks (SSDs), silicon disk drives (SDDs), ROMs, RAMs,CD-ROMs, magnetic tapes, floppy disks, and/or optical data storage, andmay be implemented in carrier waveforms (e.g., transmissions over theInternet). The foregoing detailed description should not be interpretednot as limiting but as exemplary in all aspects. The scope of thepresent invention should be defined by reasonable interpretation of theappended claims and all equivalents and changes thereto should fallwithin the scope of the invention.

What is claimed is:
 1. A method of intelligently recognizing a voice bya voice recognizing device, the method comprising: obtaining amicrophone detection signal; recognizing a user's voice from themicrophone detection signal based on a pre-learned speech recognitionmodel; and generating information related to a result of recognition ofthe user's voice, wherein the speech recognition model is updated basedon the generated speech recognition result information.
 2. The method ofclaim 1, wherein the speech recognition result information includesinformation related to whether the speech recognition succeeds.
 3. Themethod of claim 2, wherein the information related to whether the speechrecognition succeeds includes text information generated by recognizingthe voice.
 4. The method of claim 2, wherein the information related towhether the speech recognition succeeds includes information related tothe number of words included in the text information.
 5. The method ofclaim 1, further comprising performing post-processing on the generatedspeech recognition result information, wherein the speech recognitionresult information includes information related to a result of thepost-processing.
 6. The method of claim 5, wherein the informationrelated to the result of the post-processing includes informationrelated to a number of times in which the text information generated bythe post-processing is modified.
 7. The method of claim 1, wherein thespeech recognition result information includes information related to aspeed at which the speech recognition result information is generated.8. The method of claim 1, further comprising: receiving, from a network,downlink control information (DCI) used for scheduling transmission ofthe speech recognition result information; and transmitting the speechrecognition result information to the network based on the DCI.
 9. Themethod of claim 8, further comprising: performing an initial accessprocedure with the network based on a synchronization signal block(SSB); and transmitting the speech recognition result information to thenetwork via a physical uplink shared channel (PUSCH), whereindemodulation-reference signals (DM-RSs) of the SSB and the PUSCH arequasi co-located (QCL) for QCL type D.
 10. The method of claim 8,further comprising: controlling a communication module to transmit thespeech recognition result information to an artificial intelligence (AI)processor included in the network; and controlling the communicationmodule to receive AI-processed information from the AI processor,wherein the AI-processed information includes a parameter of the speechrecognition model updated based on the speech recognition resultinformation.
 11. A voice recognizing device, comprising: at least onemicrophone detecting an external signal; and a processor recognizing auser's voice from a microphone detection signal obtained via the atleast one microphone based on a pre-learned speech recognition model andgenerating information related to a result of recognition of the user'svoice, wherein the speech recognition model is updated based on thegenerated speech recognition result information.
 12. The voicerecognizing device of claim 11, wherein the speech recognition resultinformation includes information related to whether the speechrecognition succeeds.
 13. The voice recognizing device of claim 12,wherein the information related to whether the speech recognitionsucceeds includes text information generated by recognizing the voice.14. The voice recognizing device of claim 12, wherein the informationrelated to whether the speech recognition succeeds includes informationrelated to the number of words included in the text information.
 15. Thevoice recognizing device of claim 11, wherein the processor performspost-processing on the generated speech recognition result information,and wherein the speech recognition result information includesinformation related to a result of the post-processing.
 16. The voicerecognizing device of claim 15, wherein the information related to theresult of the post-processing includes information related to a numberof times in which the text information generated by the post-processingis modified.
 17. The voice recognizing device of claim 11, wherein thespeech recognition result information includes information related to aspeed at which the speech recognition result information is generated.18. The voice recognizing device of claim 11, further comprising acommunication module for performing wireless communication with anexternal device, wherein the processor receives, from a network throughthe communication module, downlink control information (DCI) used forscheduling transmission of the speech recognition result information andtransmits the speech recognition result information through thecommunication module to the network based on the DCI.
 19. The voicerecognizing device of claim 18, wherein the processor performs aninitial access procedure with the network based on a synchronizationsignal block (SSB) through the communication module and transmits,through the communication module to the network, the speech recognitionresult information via a PUSCH, and wherein demodulation-referencesignals (DM-RSs) of the SSB and the PUSCH are quasi co-located (QCL) forQCL type D.
 20. The voice recognizing device of claim 18, wherein theprocessor controls the communication module to transmit the speechrecognition result information to an artificial intelligence (AI)processor included in the network and controls the communication moduleto receive AI-processed information from the AI processor, wherein theAI-processed information includes a parameter of the speech recognitionmodel updated based on the speech recognition result information.